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Background: The Medical Certificate of Stillbirth (MCS) records data about a baby's death after 24 weeks of gestation but before birth. Major errors that could alter interpretation of the MCS were widespread in two UK-based regional studies., Methods: A multicentre evaluation was conducted, examining MCS issued 1 January 2018 to 31 December 2018 in 76 UK obstetric units. A systematic case-note review of stillbirths was conducted by Obstetric and Gynaecology trainees, generating individual 'ideal MCSs' and comparing these to the actual MCS issued. Anonymized central data analysis described rates and types of error, agreement and factors associated with major errors., Results: There were 1120 MCSs suitable for assessment, with 126 additional submitted data sets unsuitable for accuracy analysis (total 1246 cases). Gestational age demonstrated 'substantial' agreement [K = 0.73 (95% CI 0.70-0.76)]. Primary cause of death (COD) showed 'fair' agreement [K = 0.26 (95% CI 0.24-0.29)]. Major errors [696/1120; 62.1% (95% CI 59.3-64.9%)] included certificates issued for fetal demise at <24 weeks' gestation [23/696; 3.3% (95% CI 2.2-4.9%)] or neonatal death [2/696; 0.3% (95% CI 0.1-1.1%)] or incorrect primary COD [667/696; 95.8% (95% CI 94.1-97.1%)]. Of 540/1246 [43.3% (95% CI 40.6-46.1%)] 'unexplained' stillbirths, only 119/540 [22.0% (95% CI 18.8-25.7%)] remained unexplained; the majority were redesignated as either fetal growth restriction [FGR: 195/540; 36.1% (95% CI 32.2-40.3%)] or placental insufficiency [184/540; 34.1% (95% CI 30.2-38.2)]. Overall, FGR [306/1246; 24.6% (95% CI 22.3-27.0%)] was the leading primary COD after review, yet only 53/306 [17.3% (95% CI 13.5-22.1%)] FGR cases were originally attributed correctly., Conclusion: This study demonstrates widespread major errors in MCS completion across the UK. MCS should only be completed following structured case-note review, with particular attention on the fetal growth trajectory., (© The Author(s) 2022. Published by Oxford University Press on behalf of the International Epidemiological Association.)

Background Two-sample Mendelian randomization (2SMR) is an increasingly popular epidemiological method that uses genetic variants as instruments for making causal inferences. Clear reporting of methods employed in such studies is important for evaluating their underlying quality. However, the quality of methodological reporting of 2SMR studies is currently unclear. We aimed to assess the reporting quality of studies that used MR-Base, one of the most popular platforms for implementing 2SMR analysis. Methods We created a bespoke reporting checklist to evaluate reporting quality of 2SMR studies. We then searched Web of Science Core Collection, PsycInfo, MEDLINE, EMBASE and Google Scholar citations of the MR-Base descriptor paper to identify published MR studies that used MR-Base for any component of the MR analysis. Study screening and data extraction were performed by at least two independent reviewers. Results In the primary analysis, 87 studies were included. Reporting quality was generally poor across studies, with a mean of 53% (SD = 14%) of items reported in each study. Many items required for evaluating the validity of key assumptions made in MR were poorly reported: only 44% of studies provided sufficient details for assessing if the genetic variant associates with the exposure ('relevance' assumption), 31% for assessing if there are any variant-outcome confounders ('independence' assumption), 89% for the assessing if the variant causes the outcome independently of the exposure ('exclusion restriction' assumption) and 32% for assumptions of falsification tests. We did not find evidence of a change in reporting quality over time or a difference in reporting quality between studies that used MR-Base and a random sample of MR studies that did not use this platform. Conclusions The quality of reporting of two-sample Mendelian randomization studies in our sample was generally poor. Journals and researchers should consider using the STROBE-MR guidelines to improve reporting quality. [ABSTRACT FROM AUTHOR]

Background: There has been a large influx of COVID-19 seroprevalence studies, but comparability between the seroprevalence estimates has been an issue because of heterogeneities in testing platforms and study methodology. One potential source of heterogeneity is the response or participation rate.Methods: We conducted a review of participation rates (PR) in SARS-CoV-2 seroprevalence studies collected by SeroTracker and examined their effect on the validity of study conclusions. PR was calculated as the count of participants for whom the investigators had collected a valid sample, divided by the number of people invited to participate in the study. A multivariable beta generalized linear model with logit link was fitted to determine if the PR of international household and community-based seroprevalence studies was associated with the factors of interest, from 1 December 2019 to 10 March 2021.Results: We identified 90 papers based on screening and were able to calculate the PR for 35 out of 90 papers (39%), with a median PR of 70% and an interquartile range of 40.92; 61% of the studies did not report PR.Conclusions: Many SARS-CoV-2 seroprevalence studies do not report PR. It is unclear what the median PR rate would be had a larger portion not had limitations in reporting. Low participation rates indicate limited representativeness of results. Non-probabilistic sampling frames were associated with higher participation rates but may be less representative. Standardized definitions of participation rate and data reporting necessary for the PR calculations are essential for understanding the representativeness of seroprevalence estimates in the population of interest. [ABSTRACT FROM AUTHOR]

We read with interest the recent paper by Sariaslan I et al i .[1] investigating the associations between childhood family income and three outcomes observed later in life: psychiatric disorders, substance misuse and violent crime arrests. To illustrate our point, assume that childhood family income has a causal effect on the neighbourhood in which a family resides while the children are growing up: the higher the income, the more affluent the neighbourhood. [Extracted from the article]

Background Observational studies are frequently used to estimate the comparative effectiveness of different colorectal cancer (CRC) screening methods due to the practical limitations and time needed to conduct large clinical trials. However, time-varying confounders, e.g. polyp detection in the last screening, can bias statistical results. Recently, generalized methods, or G-methods, have been used for the analysis of observational studies of CRC screening, given their ability to account for such time-varying confounders. Discretization, or the process of converting continuous functions into discrete counterparts, is required for G-methods when the treatment and outcomes are assessed at a continuous scale. Development This paper evaluates the interplay between time-varying confounding and discretization, which can induce bias in assessing screening effectiveness. We investigate this bias in evaluating the effect of different CRC screening methods that differ from each other in typical screening frequency. Application First, using theory, we establish the direction of the bias. Then, we use simulations of hypothetical settings to study the bias magnitude for varying levels of discretization, frequency of screening and length of the study period. We develop a method to assess possible bias due to coarsening in simulated situations. Conclusions The proposed method can inform future studies of screening effectiveness, especially for CRC, by determining the choice of interval lengths where data are discretized to minimize bias due to coarsening while balancing computational costs. [ABSTRACT FROM AUTHOR]

This paper presents causal loop diagrams (CLDs) as tools for studying complex public health problems like health inequality. These problems often involve feedback loops—a characteristic of complex systems not fully integrated into mainstream epidemiology. CLDs are conceptual models that visualize connections between system variables. They are commonly developed through literature reviews or participatory methods with stakeholder groups. These diagrams often uncover feedback loops among variables across scales (e.g. biological, psychological and social), facilitating cross-disciplinary insights. We illustrate their use through a case example involving the feedback loop between sleep problems and depressive symptoms. We outline a typical step-by-step process for developing CLDs in epidemiology. These steps are defining a specific problem, identifying the key system variables involved, mapping these variables and analysing the CLD to find new insights and possible intervention targets. Throughout this process, we suggest triangulating between diverse sources of evidence, including domain knowledge, scientific literature and empirical data. CLDs can also be evaluated to guide policy changes and future research by revealing knowledge gaps. Finally, CLDs may be iteratively refined as new evidence emerges. We advocate for more widespread use of complex systems tools, like CLDs, in epidemiology to better understand and address complex public health problems. [ABSTRACT FROM AUTHOR]

Background: Little is known about the time course of mortality reduction following smoking cessation in Asians who have smoking behaviours distinct from their Western counterparts. We evaluated the level of reduction in all-cause, cardiovascular disease (CVD) and lung cancer mortality by years since quitting smoking, in Asia., Methods: Using Cox regression, we analysed individual participant data (n = 709 151) from 16 prospective cohorts conducted in China, Japan, Korea/Singapore, and India/Bangladesh, separately by cohorts. Cohort-specific hazard ratios (HRs) were combined using a random-effects meta-analysis., Results: During a mean follow-up of 12.0 years, 108 287 deaths were ascertained-35 658 from CVD and 7546 from lung cancer. Among Asian men, a dose-response relationship of risk reduction in deaths from all causes, CVD and lung cancer was observed with an increase in years after smoking cessation. Compared with never smokers, however, all-cause and CVD mortality among former smokers remained elevated 10-14 years after quitting [multivariable-adjusted HR (95% confidence interval (CI) = 1.25 (1.13-1.37) and 1.20 (1.02-1.41), respectively]. Lung cancer mortality stayed almost 2-fold higher than among never smokers 15-19 years after smoking cessation [1.97 (1.41-2.73)], particularly among former heavy smokers [2.62 (1.71-4.00)]. Women who quitted for ≥5 years retained a significantly elevated mortality from all causes, CVD and lung cancer. Overall patterns of the cessation-mortality associations were similar across countries., Conclusions: Our findings suggest that adverse effects of tobacco smoking persist for an extended time period, even for more than two decades, which is beyond the time windows defined in current clinical guidelines for risk assessment of lung cancer and CVD., (© The Author(s) 2021; all rights reserved. Published by Oxford University Press on behalf of the International Epidemiological Association.)

Teaching epidemiological concepts in academic settings poses a challenge due to the intricate nature of the discipline as both a science and a practice. Whereas traditional classroom-based teaching methods are commonly employed, evidence suggests they may not be the most effective approach for fostering core competencies and skills required in real-life scientific work. In this article, we describe our process of transitioning from traditional classroom teaching of epidemiology towards practice-based coaching to convey epidemiological concepts to bachelor's and master's students in Biomedicine. We chose the framework of randomized controlled trials (RCT) since they offer a great opportunity to teach epidemiological concepts in a hands-on course. This practice-based course encompasses the entire life cycle of a study, allowing students to design and conduct a short-term experiment, analyse its data and prepare a scientific paper. We provide a comprehensive overview of the course structure, content, learning objectives and course evaluation, while also discussing the advantages and disadvantages of this innovative format. Our approach offers a promising alternative to classroom teaching by incorporating practical, hands-on experiences offering students a high level of independence and self-determination, as well as facilitation and coaching by faculty. It has the potential to be applied across diverse academic settings, providing students with valuable skills and competencies in epidemiology. [ABSTRACT FROM AUTHOR]

Background In descriptive epidemiology, there are strong similarities between incidence and survival analyses. Because of the success of multidimensional penalized splines (MPSs) in incidence analysis, we propose in this pedagogical paper to show that MPSs are also very suitable for survival or net survival studies. Methods The use of MPSs is illustrated in cancer epidemiology in the context of survival trends studies that require specific statistical modelling. We focus on two examples (cervical and colon cancers) using survival data from the French cancer registries (cases 1990–2015). The dynamic of the excess mortality hazard according to time since diagnosis was modelled using an MPS of time since diagnosis, age at diagnosis and year of diagnosis. Multidimensional splines bring the flexibility necessary to capture any trend patterns while penalization ensures selecting only the complexities necessary to describe the data. Results For cervical cancer, the dynamic of the excess mortality hazard changed with the year of diagnosis in opposite ways according to age: this led to a net survival that improved in young women and worsened in older women. For colon cancer, regardless of age, excess mortality decreases with the year of diagnosis but this only concerns mortality at the start of follow-up. Conclusions MPSs make it possible to describe the dynamic of the mortality hazard and how this dynamic changes with the year of diagnosis, or more generally with any covariates of interest: this gives essential epidemiological insights for interpreting results. We use the R package survPen to do this type of analysis. [ABSTRACT FROM AUTHOR]

M-estimation is a statistical procedure that is particularly advantageous for some comon epidemiological analyses, including approaches to estimate an adjusted marginal risk contrast (i.e. inverse probability weighting and g-computation) and data fusion. In such settings, maximum likelihood variance estimates are not consistent. Thus, epidemiologists often resort to bootstrap to estimate the variance. In contrast, M-estimation allows for consistent variance estimates in these settings without requiring the computational complexity of the bootstrap. In this paper, we introduce M-estimation and provide four illustrative examples of implementation along with software code in multiple languages. M-estimation is a flexible and computationally efficient estimation procedure that is a powerful addition to the epidemiologist's toolbox. [ABSTRACT FROM AUTHOR]

This viewpoint discusses a conference paper titled: "Epidemiological research on mental morbidity in Chile", which summarizes a handful of studies from 1950s in Chile covering conditions such as alcoholism, psychosis, epilepsy, and neurosis. These were the first psychiatric epidemiological population studies conducted in the Southern hemisphere, but they are largely unknown globally. We argue that the Chilean studies have important implications for modern psychiatric epidemiology, as well as for related fields such as global mental health and mental health services research. This piece starts by describing the general sociopolitical context and presenting the main methodological features of the studies. It focuses then on three specific implications for the field: 1) promoting decoloniality in psychiatric epidemiology; 2) ensuring methodological rigor and feasibility; and 3) informing the development of mental health policy and services. [ABSTRACT FROM AUTHOR]

Background The debated link between severe respiratory syncytial virus (RSV) infection in early life and asthma has yet to be investigated within a social inequity lens. We estimated the magnitude of socioeconomic disparity in childhood asthma which would remain if no child were admitted to hospital for bronchiolitis, commonly due to RSV, during infancy. Methods The cohort, constructed from national administrative health datasets, comprised 83853 children born in Scotland between 1 January 2007 and 31 June 2008. Scottish Index for Multiple Deprivation (SIMD) was used to capture socioeconomic position. Emergency admissions for bronchiolitis before age 1 year were identified from hospital records. Yearly indicators of asthma/wheeze from ages 2 to 9 years were created using dispensing data and hospital admission records. Results Using latent class growth analysis, we identified four trajectories of asthma/wheeze: early-transient (2.2% of the cohort), early-persistent (2.0%), intermediate-onset (1.8%) and no asthma/wheeze (94.0%). The estimated marginal risks of chronic asthma (combining early-persistent and intermediate-onset groups) varied by SIMD, with risk differences for the medium and high deprivation groups, relative to the low deprivation group, of 7.0% (95% confidence interval: 3.7–10.3) and 13.0% (9.6–16.4), respectively. Using counterfactual disparity measures, we estimated that the elimination of bronchiolitis requiring hospital admission could reduce these risk differences by 21.2% (4.9–37.5) and 17.9% (10.4–25.4), respectively. Conclusions The majority of disparity in chronic asthma prevalence by deprivation level remains unexplained. Our paper offers a guide to using causal inference methods to study other plausible pathways to inequities in asthma using complex, linked administrative data. [ABSTRACT FROM AUTHOR]

Background: Quantitative bias analysis (QBA) measures study errors in terms of direction, magnitude and uncertainty. This systematic review aimed to describe how QBA has been applied in epidemiological research in 2006-19.Methods: We searched PubMed for English peer-reviewed studies applying QBA to real-data applications. We also included studies citing selected sources or which were identified in a previous QBA review in pharmacoepidemiology. For each study, we extracted the rationale, methodology, bias-adjusted results and interpretation and assessed factors associated with reproducibility.Results: Of the 238 studies, the majority were embedded within papers whose main inferences were drawn from conventional approaches as secondary (sensitivity) analyses to quantity-specific biases (52%) or to assess the extent of bias required to shift the point estimate to the null (25%); 10% were standalone papers. The most common approach was probabilistic (57%). Misclassification was modelled in 57%, uncontrolled confounder(s) in 40% and selection bias in 17%. Most did not consider multiple biases or correlations between errors. When specified, bias parameters came from the literature (48%) more often than internal validation studies (29%). The majority (60%) of analyses resulted in >10% change from the conventional point estimate; however, most investigators (63%) did not alter their original interpretation. Degree of reproducibility related to inclusion of code, formulas, sensitivity analyses and supplementary materials, as well as the QBA rationale.Conclusions: QBA applications were rare though increased over time. Future investigators should reference good practices and include details to promote transparency and to serve as a reference for other researchers. [ABSTRACT FROM AUTHOR]

Background: Rigorously designed longitudinal studies can inform how best to reduce the widening health gap between Indigenous and non-Indigenous children.Methods: A systematic review was performed to identify and present the breadth and depth of longitudinal studies reporting the health and well-being of Indigenous children (aged 0-18 years) globally. Databases were searched up to 23 June 2020. Study characteristics were mapped according to domains of the life course model of health. Risk of bias was assessed using the National Institutes of Health (NIH) Study Quality Assessment Tools. Reported level of Indigenous involvement was also appraised; PROSPERO registration CRD42018089950.Results: From 5545 citations, 380 eligible papers were included for analysis, representing 210 individual studies. Of these, 41% were located in Australia (n = 88), 22.8% in the USA (n = 42), 11.9% in Canada (n = 25) and 10.9% in New Zealand (n = 23). Research tended to focus on either health outcomes (50.9%) or health-risk exposures (43.8%); 55% of studies were graded as 'good' quality; and 89% of studies made at least one reference to the involvement of Indigenous peoples over the course of their research.Conclusions: We identified gaps in the longitudinal assessment of cultural factors influencing Indigenous child health at the macrosocial level, including connection to culture and country, intergenerational trauma, and racism or discrimination. Future longitudinal research needs to be conducted with strong Indigenous leadership and participation including holistic concepts of health. This is critical if we are to better understand the systematic factors driving health inequities experienced by Indigenous children globally. [ABSTRACT FROM AUTHOR]

The screening method provides a simple and rapid way of estimating vaccine effectiveness. This paper discusses the validity of the screening method with particular reference to bias and precision. Methods for correcting confounding, adjusting for covariates and over-dispersion, and deriving confidence limits are discussed in a modelling framework. The methods are illustrated using data on measles and pertussis vaccines. [ABSTRACT FROM AUTHOR]

Additionally, as MVP expands eligibility to Veterans who may not use the VA for health care services, the importance of self-report data becomes imperative as MVP builds the infrastructure to access external health records. For greater applicability, it is envisioned that the MVP Baseline and MVP Lifestyle Surveys will be validated in future MVP scientific projects to enhance the understanding of Veteran health and wellbeing. Data resource access As described in detail in Gaziano's MVP introduction paper[3] and MVP website [https://www.research.va.gov/mvp/], access to MVP data and/or samples is governed by the scope of MVP informed consent and VA policies and requires scientific review by appropriate VA review committees. Keywords: Health surveys; Veteran health; cohort studies; survey development; Veteran lifestyle EN Health surveys Veteran health cohort studies survey development Veteran lifestyle e1 e17 17 02/16/23 20230201 NES 230201 Key Features The Department of Veterans Affairs (VA) Million Veteran Program (MVP) is a national voluntary research programme designed to examine genetic influences on health with the goal of improving health for Veterans. [Extracted from the article]

Recent years have seen a surge of interest in stepped-wedge cluster randomized trials (SW-CRTs). SW-CRTs include several design variations and methodology is rapidly developing. Accordingly, a variety of power and sample size calculation software for SW-CRTs has been developed. However, each calculator may support only a selected set of design features and may not be appropriate for all scenarios. Currently, there is no resource to assist researchers in selecting the most appropriate calculator for planning their trials. In this paper, we review and classify 18 existing calculators that can be implemented in major platforms, such as R, SAS, Stata, Microsoft Excel, PASS and nQuery. After reviewing the main sample size considerations for SW-CRTs, we summarize the features supported by the available calculators, including the types of designs, outcomes, correlation structures and treatment effects; whether incomplete designs, cluster-size variation or secular trends are accommodated; and the analytical approach used. We then discuss in more detail four main calculators and identify their strengths and limitations. We illustrate how to use these four calculators to compute power for two real SW-CRTs with a continuous and binary outcome and compare the results. We show that the choice of calculator can make a substantial difference in the calculated power and explain these differences. Finally, we make recommendations for implementing sample size or power calculations using the available calculators. An R Shiny app is available for users to select the calculator that meets their requirements (https://douyang.shinyapps.io/swcrtcalculator/). [ABSTRACT FROM AUTHOR]

Google Scholar Crossref Search ADS PubMed WorldCat 11 Enas EA, Mohan V, Deepa M, Farooq S, Pazhoor S, Chennikkara H. The metabolic syndrome and dyslipidemia among Asian Indians: a population with high rates of diabetes and premature coronary artery disease. Tablet-based questionnaires facilitate the easier acquisition of data (eliminates the transfer of data from paper forms to electronic databases), execution of skip patterns and application of instant data-checking rules. [Extracted from the article]

Not only do cluster randomized trials require a larger sample size than individually randomized trials, they also face many additional complexities. The potential for contamination is the most commonly used justification for using cluster randomization, but the risk of contamination should be carefully weighed against the more serious problem of questionable scientific validity in settings with post-randomization identification or recruitment of participants unblinded to the treatment allocation. In this paper we provide some simple guidelines to help researchers conduct cluster trials in a way that minimizes potential biases and maximizes statistical efficiency. The overarching theme of this guidance is that methods that apply to individually randomized trials rarely apply to cluster randomized trials. We recommend that cluster randomization be only used when necessary—balancing the benefits of cluster randomization with its increased risks of bias and increased sample size. Researchers should also randomize at the lowest possible level—balancing the risks of contamination with ensuring an adequate number of randomization units—as well as exploring other options for statistically efficient designs. Clustering should always be allowed for in the sample size calculation; and the use of restricted randomization (and adjustment in the analysis for covariates used in the randomization) should be considered. Where possible, participants should be recruited before randomizing clusters and, when recruiting (or identifying) participants post-randomization, recruiters should be masked to the allocation. In the analysis, the target of inference should align with the research question, and adjustment for clustering and small sample corrections should be used when the trial includes less than about 40 clusters. [ABSTRACT FROM AUTHOR]

Methods for calculating health indicators profoundly influence understanding of and action on population health and inequities. Age-standardization can be useful and is commonly applied to account for differences in age structures when comparing health indicators across groups. Age-standardized rates have well-acknowledged limitations, including that they are relative indices for comparison, and not accurate measures of actual rates where the age structures of groups diverge. This paper explores these limitations, and demonstrates alternative approaches through a case study quantifying mortality rates within the Aboriginal and Torres Strait Islander (Indigenous) population of Australia and inequities compared with the non-Indigenous population, over 2001-16. Applying the Australian Standard Population, the Aboriginal and Torres Strait Islander age-standardized mortality rate was more than double the crude mortality rate in 2001 and 2016, inflated through high weighting of older age groups. Despite divergent population age structures, age-standardized mortality rates remain a key policy metric for measuring progress in reducing Indigenous-non-Indigenous inequities in Australia. Focusing on outcomes age-standardized to the total population can obscure inequities, and denies Aboriginal and Torres Strait Islander peoples and communities valid, actionable information about their health and well-being. Age-specific statistics convey the true magnitude of health risks and highlight high-risk subgroups. When requiring standardization, standardizing to a population-specific standard (here, an Indigenous standard) generates metrics centred around and reflective of reality for the population of focus, supporting communities' self-determination to identify priorities and informing resource allocation and service delivery. The principles outlined here apply across populations, including Indigenous and other populations internationally. [ABSTRACT FROM AUTHOR]

In our recent paper,[1] we examined associations between family income during childhood and adolescence on subsequent risks of developing psychiatric disorders, having substance misuse problems and being arrested for a violent crime. In fact, the point estimates for the sibling-comparison estimates were all close to 1, indicating no difference between the siblings regardless of their family income exposure, and the confidence intervals were narrow, indicating a high level of precision of the estimates. [Extracted from the article]

One key objective of the population health sciences is to understand why one social group has different levels of health and well-being compared with another. Whereas several methods have been developed in economics, sociology, demography, and epidemiology to answer these types of questions, a recent method introduced by Jackson and VanderWeele (2018) provided an update to decompositions by anchoring them within causal inference theory. In this paper, we demonstrate how to implement the causal decomposition using Monte Carlo integration and the parametric g-formula. Causal decomposition can help to identify the sources of differences across populations and provide researchers with a way to move beyond estimating inequalities to explaining them and determining what can be done to reduce health disparities. Our implementation approach can easily and flexibly be applied for different types of outcome and explanatory variables without having to derive decomposition equations. We describe the concepts of the approach and the practical steps and considerations needed to implement it. We then walk through a worked example in which we investigate the contribution of smoking to sex differences in mortality in South Korea. For this example, we provide both pseudocode and R code using our package, cfdecomp. Ultimately, we outline how to implement a very general decomposition algorithm that is grounded in counterfactual theory but still easy to apply to a wide range of situations. [ABSTRACT FROM AUTHOR]

Researchers faced with incomplete data are encouraged to consider whether their data are 'missing completely at random' (MCAR), 'missing at random' (MAR) or 'missing not at random' (MNAR) when planning their analysis. However, there are two major problems with this classification as originally defined by Rubin in the 1970s. First, when there are missing data in multiple variables, the plausibility of the MAR assumption is difficult to assess using substantive knowledge and is more stringent than is generally appreciated. Second, although MCAR and MAR are sufficient conditions for consistent estimation with specific methods, they are not necessary conditions and therefore this categorization does not directly determine the best approach for handling the missing data in an analysis. How best to handle missing data depends on the assumed causal relationships between variables and their missingness, and what these relationships imply in terms of the 'recoverability' of the target estimand (the population parameter that encodes the answer to the underlying research question). Recoverability is defined as whether the estimand can be consistently estimated from the patterns and associations in the observed data without needing to invoke external information on the extent to which the distribution of missing values might differ from that of observed values. In this manuscript we outline an approach for deciding which method to use to handle multivariable missing data in an analysis, using directed acyclic graphs to depict missingness assumptions and determining the implications in terms of recoverability of the target estimand. [ABSTRACT FROM AUTHOR]

This article is a response to Dr. Wakeford's interest in a previous paper on solid cancer mortality among US nuclear workers. The authors of the response agree that their report provides new and relevant information on the risks of protracted low-dose ionizing radiation. They chose to publish risk estimates that focus on contemporary workers employed in 1960 or later, as these workers have lower average exposures and higher-quality exposure measurement records compared to earlier hires. The authors acknowledge that more research is needed to understand the differences in risk estimates between early and later hires, but this investigation was beyond the scope of their paper. They emphasize that workers exposed to protracted low-dose ionizing radiation are at risk of solid cancer and some subtypes. The study was approved by the National Institute for Occupational Safety and Health Institutional Review Board, and data availability is subject to US data protections and regulations. [Extracted from the article]

Background The World Mental Health-Composite International Diagnostic Interview (CIDI) 3.0, originally in English, is a fully-structured interview designed for the assessment of mental disorders. Although Arabic translations of CIDI from countries like Lebanon and Iraq exist, a Modern Standard Arabic translation was developed to suit the Saudi population. While the translation model used in the present paper has been used to translate instruments in Asian and European languages, there is no study to the best of our knowledge which has used this specific model to translate a validated instrument from English to Arabic. Case presentation This paper describes the Saudi adaptation of CIDI 3.0. The TRAPD team translation model—comprising of translation, review, adjudication, pretesting and documentation—was implemented to carry out the Saudi adaptation of CIDI 3.0. Pretests involving cognitive interviewing and pilot study led to translation revisions which consequently confirmed that Saudi respondents had a good understanding of various items of the instrument. The adaptation procedures for the Saudi CIDI 3.0 were well documented and the instrument was linguistically validated with the Saudi population. Conclusions The TRAPD model was successfully implemented to adapt the CIDI 3.0 to be used as the main survey instrument for the Saudi National Mental Health Survey, findings of which will provide health policy makers mental health indicators for health decision making and planning. Key messages The CIDI 3.0 is a resourceful tool for examining mental health issues in the GCC. [ABSTRACT FROM AUTHOR]

Background The COVID-19 outbreak is increasing and spreading rapidly globally, with over 20 million cases and 800, 000 thousand deaths reported in 216 countries as of 28th August 2020. Since the report of the index case in Nigeria in February 2020 by the Nigeria Center for Disease Control (NCDC), daily records of confirmed cases have been reported in all states in the country. On 3rd April 2020, an outbreak of Coronavirus disease-2019 (COVID-19) was confirmed in Ondo State, Southwest Nigeria. Field investigations were conducted by the State Ministry of Health (MoH) to identify and confirm additional cases. This paper provides the outcome of the epidemiological investigation of the outbreak to further guide outbreak response activities. Methods Outbreak settings Ondo State is in the South-West Zone of Nigeria with her capital at Akure. The State is situated between longitudes 40 151E and 60 001E of the Greenwich median and latitudes 50 451N and 70 451 N, which are to the North of the equator in the Southwestern geopolitical zones of the country. Field Investigation The investigation was conducted as part of outbreak control and response measures hence permission to conduct the study was obtained from the Ondo State Ministry of Health (OSMoH). Following an alert from clinicians at a government-owned Hospital, Akure, Ondo State on 30 March 2020, the index case of COVID-19, a 34-year-old male with recent travel history to India was investigated. He arrived at Ondo State on 21st March 2020 and presented at the hospital with cough, sore throat and running nose with the onset of symptoms on 23rd March 2020. The State public health emergency Rapid Response Team (RRT) comprising of Commissioner for Health, State Epidemiologist, State and LGA surveillance officers, and health development partners in the state visited the hospital to investigate and implement public health response. Nasopharygeal and oropharyngeal samples were collected and tested for COVID-19, and returned positive from the national reference laboratory on the 3rd of April 2020. Operational definitions During the onset of the outbreak, three categories of case definitions for COVID-19 were used to guide the outbreak investigations according to the Nigeria Center for Diseases Control (NCDC) guidelines. Suspect case: (1) This is a patient with acute respiratory illness (fever and at least one sign/ symptom of respiratory disease (e.g. cough, shortness of breath) and a history of travel to or residence in a country/area or territory reporting local transmission of COVID-19 disease during the 14 days prior to symptom onset; (2) or a patient/health care worker with any acute respiratory illness and has been in contact with a confirmed COVID-19 case in the last 14 days prior to the onset of symptoms; (3) or a patient with a severe acute respiratory infection (fever and at least one sign/symptom of respiratory disease (e.g. cough, shortness of breath) and requiring hospitalization and with no other aetiology that fully explains the clinical presentation; (4) or a case for whom testing for COVID-19 is inconclusive. Confirmed case: A person with laboratory confirmation of COVID-19 infection, irrespective of clinical signs and symptoms. Probable case: Any suspected case for whom testing for COVID-19 is indeterminate test result or for whom testing was positive on a pan-coronavirus assay Data analysis The State COVID-19 line-list and case investigation forms of all COVID-19 cases from 19th March to 9th August 2020 were retrieved from the state disease surveillance unit, information on key variables were extracted and exported into SPSS version 20 and analyzed. Descriptive statistics such as frequency table, mean (standard deviation) and charts were used to describe key variables including LGA, age, sex, occupation and education and clinical conditions of cases. The week of report of cases and outcomes were used to generate the epidemic curve. The Chi-square test was used to compare categorical variables including the socio-demographic characteristics, clinical condition and outcome of cases. Two-sided P-values <0.05 were considered statistically significant. Results Socio-demographic characteristics of cases A total of 4353 suspected cases were reported and tested for COVID-19, of which 1316 COVID-19 cases were confirmed, with a case fatality rate of 2.2% recorded in 7 of the 17 Local Government Areas that reported at least a confirmed case (Figure 1). Most of the confirmed cases (1169; 88.8%), resides in urban areas (LGAs) (Table 1). Majority [1110 (84.3%)] were within the age group 20 to 59 years, with a mean age of 37.8 ±14.8 years. Males (713; 54.2%) were more affected compare to females (603; 45.8). More than three quarters (1009; 76.7%) of the cases had a tertiary level of education. Health care workers (404; 30.7%) were most affected compared to other professionals as shown in Table 1. 1170 Figure 1: Open in new tab Download slide Distribution of suspected and confirmed COVID-19 cases in Ondo state, March to August 2020 1170 Figure 1: Open in new tab Download slide Distribution of suspected and confirmed COVID-19 cases in Ondo state, March to August 2020 1170 Table 1: Socio-demographic characteristics of respondents N = 1316 Variable.  Frequency.  Percentage.  Age in years       <5  18  1.4  5-19  84  6.4  20-39  666  50.6  40-59  444  33.7  60-79  96  7.3  ≥80  8  0.6  Gender       Male  713  54.2  Female  603  45.8  Location of cases       Rural  147  11.2  Urban  1169  88.8  Type of Education       None  15  1.1  Nursery  8  0.6  Primary  80  6.1  Secondary  204  15.5  Tertiary  1009  76.7  Occupation       Health workers  404  30.7  Businessman and woman  225  17.1  Civil servant  168  12.8  Others  182  13.8  Pupil/Student  129  9.8  Political office Holders  77  5.9  Engineer  48  3.6  Religious/traditional leader  24  1.8  Banker  24  1.8  Security officer  35  2.7  Variable.  Frequency.  Percentage.  Age in years       <5  18  1.4  5-19  84  6.4  20-39  666  50.6  40-59  444  33.7  60-79  96  7.3  ≥80  8  0.6  Gender       Male  713  54.2  Female  603  45.8  Location of cases       Rural  147  11.2  Urban  1169  88.8  Type of Education       None  15  1.1  Nursery  8  0.6  Primary  80  6.1  Secondary  204  15.5  Tertiary  1009  76.7  Occupation       Health workers  404  30.7  Businessman and woman  225  17.1  Civil servant  168  12.8  Others  182  13.8  Pupil/Student  129  9.8  Political office Holders  77  5.9  Engineer  48  3.6  Religious/traditional leader  24  1.8  Banker  24  1.8  Security officer  35  2.7  Open in new tab 1170 Table 1: Socio-demographic characteristics of respondents N = 1316 Variable.  Frequency.  Percentage.  Age in years       <5  18  1.4  5-19  84  6.4  20-39  666  50.6  40-59  444  33.7  60-79  96  7.3  ≥80  8  0.6  Gender       Male  713  54.2  Female  603  45.8  Location of cases       Rural  147  11.2  Urban  1169  88.8  Type of Education       None  15  1.1  Nursery  8  0.6  Primary  80  6.1  Secondary  204  15.5  Tertiary  1009  76.7  Occupation       Health workers  404  30.7  Businessman and woman  225  17.1  Civil servant  168  12.8  Others  182  13.8  Pupil/Student  129  9.8  Political office Holders  77  5.9  Engineer  48  3.6  Religious/traditional leader  24  1.8  Banker  24  1.8  Security officer  35  2.7  Variable.  Frequency.  Percentage.  Age in years       <5  18  1.4  5-19  84  6.4  20-39  666  50.6  40-59  444  33.7  60-79  96  7.3  ≥80  8  0.6  Gender       Male  713  54.2  Female  603  45.8  Location of cases       Rural  147  11.2  Urban  1169  88.8  Type of Education       None  15  1.1  Nursery  8  0.6  Primary  80  6.1  Secondary  204  15.5  Tertiary  1009  76.7  Occupation       Health workers  404  30.7  Businessman and woman  225  17.1  Civil servant  168  12.8  Others  182  13.8  Pupil/Student  129  9.8  Political office Holders  77  5.9  Engineer  48  3.6  Religious/traditional leader  24  1.8  Banker  24  1.8  Security officer  35  2.7  Open in new tab Admission and clinical conditions of confirmed cases From Table 2 , 88 (6.7%) of the confirmed cases were admitted as inpatient during investigation, while 325 (24.7%) were symptomatic. The first symptom reported by cases were as follows; cough (98; 30.2%), fever (74; 22.8%), headache (30; 9.2%), runny nose (39; 12.0%), sore throat (24; 7.4%) and difficulty in breathing (15; 4.6%). 1170 Table 2: Admission and clinical conditions of confirmed cases.  Frequency.  Percentage.  Admitted as inpatient before lab testing   Yes  88  6.7  No  1228  93.3  Symptomatic       Yes  325  24.7  No  991  75.3  First symptom reported by respondents (n = 325)   Cough  98  30.2  Fever  74  22.8  Headache  30  9.2  Runny nose  39  12.0  Sore throat/pharyngitis  24  7.4  Difficulty breathing  15  4.6  New loss of taste  9  2.7  New loss of smell  8  2.5  Chest pain  7  2.2  General Body weakness  6  1.8  Others  15  4.6  Outcome       Alive  1287  97.8  Dead  29  2.2 .  Frequency.  Percentage.  Admitted as inpatient before lab testing   Yes  88  6.7  No  1228  93.3  Symptomatic       Yes  325  24.7  No  991  75.3  First symptom reported by respondents (n = 325)   Cough  98  30.2  Fever  74  22.8  Headache  30  9.2  Runny nose  39  12.0  Sore throat/pharyngitis  24  7.4  Difficulty breathing  15  4.6  New loss of taste  9  2.7  New loss of smell  8  2.5  Chest pain  7  2.2  General Body weakness  6  1.8  Others  15  4.6  Outcome       Alive  1287  97.8  Dead  29  2.2  Others= abdominal pain; chills/sweats; joint pain; inability to walk; Nausea Open in new tab 1170 Table 2: Admission and clinical conditions of confirmed cases.  Frequency.  Percentage.  Admitted as inpatient before lab testing   Yes  88  6.7  No  1228  93.3  Symptomatic       Yes  325  24.7  No  991  75.3  First symptom reported by respondents (n = 325)   Cough  98  30.2  Fever  74  22.8  Headache  30  9.2  Runny nose  39  12.0  Sore throat/pharyngitis  24  7.4  Difficulty breathing  15  4.6  New loss of taste  9  2.7  New loss of smell  8  2.5  Chest pain  7  2.2  General Body weakness  6  1.8  Others  15  4.6  Outcome       Alive  1287  97.8  Dead  29  2.2 .  Frequency.  Percentage.  Admitted as inpatient before lab testing   Yes  88  6.7  No  1228  93.3  Symptomatic       Yes  325  24.7  No  991  75.3  First symptom reported by respondents (n = 325)   Cough  98  30.2  Fever  74  22.8  Headache  30  9.2  Runny nose  39  12.0  Sore throat/pharyngitis  24  7.4  Difficulty breathing  15  4.6  New loss of taste  9  2.7  New loss of smell  8  2.5  Chest pain  7  2.2  General Body weakness  6  1.8  Others  15  4.6  Outcome       Alive  1287  97.8  Dead  29  2.2  Others= abdominal pain; chills/sweats; joint pain; inability to walk; Nausea Open in new tab Figure 2 described the epidemic curve of the outbreak from March to August 2020. The index case was confirmed on April 4, 2020. Thereafter, there was a surge in the number of confirmed COVID-19 cases with the outbreak reaching its peak on July 2, 2020. Afterwards, fluctuations in the number of cases were observed before a steady decline was recorded between August 3, 2020 and August 9, 2020. 1170 Figure 2 Open in new tab Download slide Epi-Curve of confirmed cases of COVID-19 in Ondo State, March to August 2020 1170 Figure 2 Open in new tab Download slide Epi-Curve of confirmed cases of COVID-19 in Ondo State, March to August 2020 Association between socio-demographic characteristics, clinical conditions and outcomes of cases In Table 3 , significant proportion of death occurred among cases within the age group 60 years and above (14; 13.5%) compared to other age groups (p < 0.001). Death occurred more among males (26; 3.6%) compared to the females (3; 0.5%) (p < 0.001). Furthermore, symptomatic cases had higher proportion (27; 8.3%) of deaths compared to asymptomatic cases (2; 0.2%) (P < 0.001). Among the symptomatic cases, a high proportion of death was found among those with difficulty in breathing (3; 20%), fever (11; 14.9%), new loss of taste (1; 11.1%), cough (9; 9.2%) and sore throat (2; 8.3%) (P < 0.001) 1170 Table 3: Association between socio-demographic characteristics and outcomes of cases Variable.  Outcome. .  Total.  P-value.  Dead.  Alive.  Age in years           <5  0 (0.0)  18 (100.0)  18  <0.001  5-19  0 (0.0)  84 (100.0)  84    20-39  2 (0.3)  664 (99.7)  666    40-59  13 (2.9)  431 (97.1)  444    ≥60  14 (13.5)  90 (86.5)  104    Gender           Male  26 (3.6)  687 (96.4)  713  <0.001  Female  3 (0.5)  600 (99.5)  603    Location of cases           Rural  5 (3.4)  142 (96.6)  147  0.294  Urban  24 (2.1)  1145 (97.9)  1169    Type of Education           None  1 (6.7)  14 (93.3)  15  0.630  Nursery  0 (0.0)  8 (100.0)  8    Primary  3 (3.8)  77 (96.2)  80    Secondary  4 (2.0)  200 (98.0)  204    Tertiary  21 (2.1)  988 (97.9)  1009    Occupation           Health workers  2 (0.5)  402 (99.5)  24    Businessman and woman  14 (6.2)  211 (93.8)  225    Civil servant  1 (0.6)  167 (99.4)  168  <0.001  Others  10 (5.5)  172 (94.5)  182    Pupil/Student  0 (0.0)  129 (100.0)  129    Politician  0 (0.0)  77 (100.0)  77    Engineer  0 (0.0)  48 (100.0)  48    Religious/traditional ruler  2 (8.3)  22 (91.7)  24    Banker  0 (0.0)  24 (100.0)  24    Security officer  0 (0.0)  35 (100.0)  35    Symptomatic           Yes  27 (8.3)  298 (91.7)  325  <0.001  No  2 (0.2)  989 (99.8)  991    First symptom       N = 325    Cough  9 (9.2)  89 (90.8)  98    Fever  11 (14.9)  63 (85.1)  74  <0.001  Headache  1 (3.3)  29 (96.7)  30    Running nose  0 (0.0)  39 (100.0)  39    Sore throat  2 (8.3)  22 (91.7)  24    New loss of taste  1 (11.1)  8 (88.9)  9    Difficulty in breathing  3 (20.0)  12 (80.0)  15    New loss of smell  0 (0.0)  5 (100.0)  5    Others  0 (0.0)  31 (100.0)  31    Variable.  Outcome. .  Total.  P-value.  Dead.  Alive.  Age in years           <5  0 (0.0)  18 (100.0)  18  <0.001  5-19  0 (0.0)  84 (100.0)  84    20-39  2 (0.3)  664 (99.7)  666    40-59  13 (2.9)  431 (97.1)  444    ≥60  14 (13.5)  90 (86.5)  104    Gender           Male  26 (3.6)  687 (96.4)  713  <0.001  Female  3 (0.5)  600 (99.5)  603    Location of cases           Rural  5 (3.4)  142 (96.6)  147  0.294  Urban  24 (2.1)  1145 (97.9)  1169    Type of Education           None  1 (6.7)  14 (93.3)  15  0.630  Nursery  0 (0.0)  8 (100.0)  8    Primary  3 (3.8)  77 (96.2)  80    Secondary  4 (2.0)  200 (98.0)  204    Tertiary  21 (2.1)  988 (97.9)  1009    Occupation           Health workers  2 (0.5)  402 (99.5)  24    Businessman and woman  14 (6.2)  211 (93.8)  225    Civil servant  1 (0.6)  167 (99.4)  168  <0.001  Others  10 (5.5)  172 (94.5)  182    Pupil/Student  0 (0.0)  129 (100.0)  129    Politician  0 (0.0)  77 (100.0)  77    Engineer  0 (0.0)  48 (100.0)  48    Religious/traditional ruler  2 (8.3)  22 (91.7)  24    Banker  0 (0.0)  24 (100.0)  24    Security officer  0 (0.0)  35 (100.0)  35    Symptomatic           Yes  27 (8.3)  298 (91.7)  325  <0.001  No  2 (0.2)  989 (99.8)  991    First symptom       N = 325    Cough  9 (9.2)  89 (90.8)  98    Fever  11 (14.9)  63 (85.1)  74  <0.001  Headache  1 (3.3)  29 (96.7)  30    Running nose  0 (0.0)  39 (100.0)  39    Sore throat  2 (8.3)  22 (91.7)  24    New loss of taste  1 (11.1)  8 (88.9)  9    Difficulty in breathing  3 (20.0)  12 (80.0)  15    New loss of smell  0 (0.0)  5 (100.0)  5    Others  0 (0.0)  31 (100.0)  31    Others = chest pain, diarrhea, fatigue, joint pain, malaise, nausea, vomiting Open in new tab 1170 Table 3: Association between socio-demographic characteristics and outcomes of cases Variable.  Outcome. .  Total.  P-value.  Dead.  Alive.  Age in years           <5  0 (0.0)  18 (100.0)  18  <0.001  5-19  0 (0.0)  84 (100.0)  84    20-39  2 (0.3)  664 (99.7)  666    40-59  13 (2.9)  431 (97.1)  444    ≥60  14 (13.5)  90 (86.5)  104    Gender           Male  26 (3.6)  687 (96.4)  713  <0.001  Female  3 (0.5)  600 (99.5)  603    Location of cases           Rural  5 (3.4)  142 (96.6)  147  0.294  Urban  24 (2.1)  1145 (97.9)  1169    Type of Education           None  1 (6.7)  14 (93.3)  15  0.630  Nursery  0 (0.0)  8 (100.0)  8    Primary  3 (3.8)  77 (96.2)  80    Secondary  4 (2.0)  200 (98.0)  204    Tertiary  21 (2.1)  988 (97.9)  1009    Occupation           Health workers  2 (0.5)  402 (99.5)  24    Businessman and woman  14 (6.2)  211 (93.8)  225    Civil servant  1 (0.6)  167 (99.4)  168  <0.001  Others  10 (5.5)  172 (94.5)  182    Pupil/Student  0 (0.0)  129 (100.0)  129    Politician  0 (0.0)  77 (100.0)  77    Engineer  0 (0.0)  48 (100.0)  48    Religious/traditional ruler  2 (8.3)  22 (91.7)  24    Banker  0 (0.0)  24 (100.0)  24    Security officer  0 (0.0)  35 (100.0)  35    Symptomatic           Yes  27 (8.3)  298 (91.7)  325  <0.001  No  2 (0.2)  989 (99.8)  991    First symptom       N = 325    Cough  9 (9.2)  89 (90.8)  98    Fever  11 (14.9)  63 (85.1)  74  <0.001  Headache  1 (3.3)  29 (96.7)  30    Running nose  0 (0.0)  39 (100.0)  39    Sore throat  2 (8.3)  22 (91.7)  24    New loss of taste  1 (11.1)  8 (88.9)  9    Difficulty in breathing  3 (20.0)  12 (80.0)  15    New loss of smell  0 (0.0)  5 (100.0)  5    Others  0 (0.0)  31 (100.0)  31    Variable.  Outcome. .  Total.  P-value.  Dead.  Alive.  Age in years           <5  0 (0.0)  18 (100.0)  18  <0.001  5-19  0 (0.0)  84 (100.0)  84    20-39  2 (0.3)  664 (99.7)  666    40-59  13 (2.9)  431 (97.1)  444    ≥60  14 (13.5)  90 (86.5)  104    Gender           Male  26 (3.6)  687 (96.4)  713  <0.001  Female  3 (0.5)  600 (99.5)  603    Location of cases           Rural  5 (3.4)  142 (96.6)  147  0.294  Urban  24 (2.1)  1145 (97.9)  1169    Type of Education           None  1 (6.7)  14 (93.3)  15  0.630  Nursery  0 (0.0)  8 (100.0)  8    Primary  3 (3.8)  77 (96.2)  80    Secondary  4 (2.0)  200 (98.0)  204    Tertiary  21 (2.1)  988 (97.9)  1009    Occupation           Health workers  2 (0.5)  402 (99.5)  24    Businessman and woman  14 (6.2)  211 (93.8)  225    Civil servant  1 (0.6)  167 (99.4)  168  <0.001  Others  10 (5.5)  172 (94.5)  182    Pupil/Student  0 (0.0)  129 (100.0)  129    Politician  0 (0.0)  77 (100.0)  77    Engineer  0 (0.0)  48 (100.0)  48    Religious/traditional ruler  2 (8.3)  22 (91.7)  24    Banker  0 (0.0)  24 (100.0)  24    Security officer  0 (0.0)  35 (100.0)  35    Symptomatic           Yes  27 (8.3)  298 (91.7)  325  <0.001  No  2 (0.2)  989 (99.8)  991    First symptom       N = 325    Cough  9 (9.2)  89 (90.8)  98    Fever  11 (14.9)  63 (85.1)  74  <0.001  Headache  1 (3.3)  29 (96.7)  30    Running nose  0 (0.0)  39 (100.0)  39    Sore throat  2 (8.3)  22 (91.7)  24    New loss of taste  1 (11.1)  8 (88.9)  9    Difficulty in breathing  3 (20.0)  12 (80.0)  15    New loss of smell  0 (0.0)  5 (100.0)  5    Others  0 (0.0)  31 (100.0)  31    Others = chest pain, diarrhea, fatigue, joint pain, malaise, nausea, vomiting Open in new tab Conclusions The outcome of this investigation indicating high transmission among urban residence and health care workers are key public health concerns in the response to the COVID-19 outbreak in Ondo State, Nigeria. Furthermore, high case mortality among the older age groups requires public health intervention. Thus, we recommend intensified risk communication, enhanced surveillance activities, and use of community structures such as community and religious leaders, market and commercial vehicles associations, Ward Development Committee (WDC) and Village Development Committee (VDC) to ensure compliance with public health COVID-19 preventive measures particularly in the urban areas and among those facing a high risk of death. Furthermore, there is a need to prioritize public health interventions including training and vaccination among the vulnerable groups including health care workers who serve as front liners during case investigation, testing and case management. Key messages Enforcement of public health preventive measures particularly in urban settings, and supporting government to strengthen and monitor Infection Prevention and Control practices in hospital settings. [ABSTRACT FROM AUTHOR]

And this need becomes especially urgent if sibling designs are to inform policy-making, as proposed by Sariaslan I et al. i Author contributions N.H.R. T.L. and A.H.P. discussed the paper by Sariaslan I et al. i [1] and agreed on an appropriate response. Even though the sibling groups carry the entire causal conclusion of the article, we do not find a single table describing the distribution of within-family income differences, or outcome differences. Income differences are most likely smaller in Finland than in most other countries, and the causes and consequences of such income differences may be very different under different societal models and/or in vulnerable groups. [Extracted from the article]

I have worked in research, teaching and consultancy positions in Australia, New Zealand and the UK, and have been an editor of the I IJE i since 2020. Google Scholar Crossref Search ADS PubMed WorldCat 3 Kumar V, Janusic T, Leeder S, Sitas F. Has the IJE's Cohort Profile series achieved its aims?. [Extracted from the article]

Background: Coffee consumption has been associated with several adverse pregnancy outcomes, although data from randomized-controlled trials are lacking. We investigate whether there is a causal relationship between coffee consumption and miscarriage, stillbirth, birthweight, gestational age and pre-term birth using Mendelian randomization (MR).Methods: A two-sample MR study was performed using summary results data from a genome-wide association meta-analysis of coffee consumption (N = 91 462) from the Coffee and Caffeine Genetics Consortium. Outcomes included self-reported miscarriage (N = 49 996 cases and 174 109 controls from a large meta-analysis); the number of stillbirths [N = 60 453 from UK Biobank (UKBB)]; gestational age and pre-term birth (N = 43 568 from the 23andMe, Inc cohort) and birthweight (N = 297 356 reporting own birthweight and N = 210 248 reporting offspring's birthweight from UKBB and the Early Growth Genetics Consortium). Additionally, a one-sample genetic risk score (GRS) analysis of coffee consumption in UKBB women (N up to 194 196) and the Avon Longitudinal Study of Parents and Children (N up to 6845 mothers and 4510 children) and its relationship with offspring outcomes was performed.Results: Both the two-sample MR and one-sample GRS analyses showed no change in risk of sporadic miscarriages, stillbirths, pre-term birth or effect on gestational age connected to coffee consumption. Although both analyses showed an association between increased coffee consumption and higher birthweight, the magnitude of the effect was inconsistent.Conclusion: Our results suggest that coffee consumption during pregnancy might not itself contribute to adverse outcomes such as stillbirth, sporadic miscarriages and pre-term birth or lower gestational age or birthweight of the offspring. [ABSTRACT FROM AUTHOR]

Focus of Presentation Cohort study recruitment can be complex, often requiring extensive pre-screening to recruit sufficient numbers of exposed and unexposed participants. We discuss a prospective study of the impact of racism on adult health in New Zealand (NZ), with emphasis on propensity-score based sampling and recruitment methods accessing participants from a national survey (NZ Health Survey 2017/18, n = 12,530 eligible adults). Discussion will cover sampling design, response rate, and confounder balance in the final sample. Key empirical results will be summarised. Findings The NZHS provided a sampling frame with complete baseline exposure and covariate data, giving n = 2,099 exposed individuals (reported racial discrimination on NZHS). A propensity-score model (stratified by ethnicity) allowed invitation of unexposed individuals balanced on key sociodemographic confounders. Recruitment used postal invitations with telephone follow-up: individuals could respond by paper survey, online questionnaire or telephone interview. Response rate was 54%, with comparable rates in exposed/unexposed individuals, with key sociodemographic factors well-balanced by exposure status. Conclusions/Implications Racism is an important determinant of health inequity, with limited prospective research in New Zealand. Our approach enabled appropriate recruitment from a sampling frame with baseline exposure status (NZHS), including allowance for exposure variability by ethnicity. Propensity-score matching on baseline covariates allowed for balance on key confounders at invitation, with balance maintained in the final sample. Key messages Secondary sampling from large national surveys can provide efficient recruitment for prospective studies. We achieved a highly satisfactory response rate, and propensity-score based sampling substantially balanced confounders between exposed and unexposed groups, enhancing study validity. [ABSTRACT FROM AUTHOR]

Introduction The Nigeria government, supported by its development partners conducted several vaccination campaigns involving various antigens across the country in 2019. Majority of the states in the North including the Federal Capital Territory (FCT) implemented the Integrated Measles and Men A vaccination campaign (IVC) which itself was an innovation given that both campaigns were initially planned as stand-alone campaigns. The integration of these campaigns came with additional planning, new implementation strategies and challenges. This paper aims to highlight the key drivers of FCT's performance in achieving greater than 90% vaccination coverage for both antigens and the challenges of the integrated campaign in FCT. Methods A baseline review of previous campaign reports, micro plans and tally sheet data was conducted to identify challenges of previous SIAs in FCT, best practices and lessons learnt. A preliminary analysis of available data revealed a shortfall in resources early enough in the planning phase. Notably, was a shortfall of about 300 vaccination teams for the implementation of the Measles vaccination campaign (as a standalone campaign) as well as shortfall in cold chain equipment and logistic funds. We leveraged on the thrice weekly technical coordination meetings to review these challenges to seek ways to mitigate them. Result Strategic efforts to address the identified gaps led to the state sponsoring an additional 354 vaccination teams to support implementation by leveraging on the SOML funds. Staggering the campaign was key to ensuring adequacy of CCEOPs to address the increase in the number required due to the integration. The additional state sponsored team vaccinated 121777 and 123588 children who otherwise would have been missed with the Measles antigen with the Men A antigen respectively. Conclusion Strategic resolution of identified challenges was pivotal to FCT's performance of greater than 90% vaccination coverages for both antigens. Key words Measles, Meningitis A, Integrated, Vaccination, Campaign [ABSTRACT FROM AUTHOR]

Background Intake of macronutrients and its components are associated with mortality and morbidity. Isocaloric substitution analysis is a tool to examine how changing the source of energy intake (e.g. from saturated to monounsaturated fat) is associated with health. However, conventional methods assume linearity, which may be untrue in many cases. This paper presents a nonlinear alternative, using UK Biobank data as an example data set. Methods Nonlinear isocaloric substitution analysis was conducted using penalised cubic splines in Cox proportional hazard models. In the UK Biobank, 195,658 participants completed at least one dietary questionnaire and were included in the analyses. Diet was assessed using the Oxford WebQ, a web-based 24-hour recall questionnaire. Prospective all-cause mortality was derived from on linked death records. Results More than half of the associations between macronutrient intake and all-cause mortality were nonlinear. Nonlinear isocaloric substitution analysis provides effect sizes conditional on the current intake, while conventional analysis provides an average over the whole range of intake. For example, conventional isocaloric substitution estimated no effect of replacing sugar with starch. However, the nonlinear method revealed that replacing sugar with starch was associated with a higher risk when the current starch intake was greater than 30% of total energy, and with a lower risk when current intake was less than 25%. Conclusions Nonlinear isocaloric substitution provides an alternative to conventional isocaloric substitution analysis when the underlying association is nonlinear. Key messages Nonlinear associations are common in nutritional epidemiology. In such cases, nonlinear isocaloric substitution analyses should be used. [ABSTRACT FROM AUTHOR]

At LSHTM, Valerie established EMU (the Epidemiological Monitoring Unit), the name reflecting her antipodean roots. Remembering Valerie Beral (1946-2022) Valerie Beral first crossed my path in the early 1970s in Papua New Guinea (PNG). [Extracted from the article]

Motivation Combination of multiple datasets is routine in modern epidemiology. However, studies may have measured different sets of variables; this is often inefficiently dealt with by excluding studies or dropping variables. Multilevel multiple imputation methods to impute these 'systematically' missing data (as opposed to 'sporadically' missing data within a study) are available, but problems may arise when many random effects are needed to allow for heterogeneity across studies. We show that the Bayesian IMputation and Analysis Model (BIMAM) implemented in our tool works well in this situation. General features BIMAM performs imputation and analysis simultaneously. It imputes both binary and continuous systematically and sporadically missing data, and analyses binary and continuous outcomes. BIMAM is a user-friendly, freely available tool that does not require knowledge of Bayesian methods. BIMAM is an R Shiny application. It is downloadable to a local machine and it automatically installs the required freely available packages (R packages, including R2MultiBUGS and MultiBUGS). Availability BIMAM is available at [ www.alecstudy.org/bimam ]. [ABSTRACT FROM AUTHOR]

Background The oral cavity is reported to be a key focus of physical injury in family violence (FV). Our purpose is to explore the potential opportunity for dentists to assist in the prevention of serious injuries in FV. This paper aims to describe the patterns of orofacial injuries in FV homicides. Methods All FV homicides in Victoria, Australia January 2006-December 2018, were identified amongst closed cases of assaults screened for eligible victim-offender relationships. Epidemiologic trends in FV in the Victorian population over the 12-year period were determined. Sociodemographic, interpersonal, incident and injury characteristics including ICD-10 coding were studied and compared across facial and non-facial injury FV subgroups. Results There was a non-significant downward trend in FV homicide over the period. Of 170 adult cases, 150 were included for facial injury analysis. Of these, 117 (78%) showed orofacial injuries in the 12-year period. Two-step cluster analysis revealed blunt force and threat to breathing injury mechanisms to be significantly associated with facial FV and sharp force with nonfacial FV. Among the additional 26 child homicides, descriptive analysis elucidated patterns in 20 cases eligible for facial injury analysis. Coding limitations were found for the FV homicide cases. Conclusions This population study reports significant involvement of the orofacial region in the FV homicide population during the 12-year study period, potentially informing dental practice and the related policy framework in Victoria and internationally. Key messages The significant involvement of the orofacial region in FV homicides may inform optimal intervention outcomes in FV in Victoria. [ABSTRACT FROM AUTHOR]

Background Everyday 2591 elderly people die due to injury worldwide. The complications and consequences of injury is different and worse than that of other age-groups. Yet little is known about the injury scenario of elderly population of Bangladesh. This paper aim to describe the epidemiology of geriatric injury of Bangladesh which will help policy-makers to undertake interventions for this vulnerable-group. Methods Bangladesh Health and Injury Survey 2016, a nationwide cross-sectional survey was carried out among 299,216 residents to identify the injury-related mortality and morbidity. Injury data among the population aged 60-years and above was extracted and analyzed for this study. Results Injury was found to cause 3.9% of the total deaths among elderly population of Bangladesh. Mortality and morbidity rate due to injury was found 157 (95%CI 112- 216) per 100,000 elderly populations and 129 (95%CI 124-133) per 1000 elderly population respectively. Fatality rate was 182.6 among urban population and 147 among rural population. Fall was identified as the leading cause of injury deaths and morbidity. It caused 47.2% of all fatality followed by Transport-injury (19.4%) and Suicide (13.9%). Among all fatally injured, 72.2% victims received treatment from health-service providers and among these 58.3% (n = 21) went to registered doctors. Conclusions Fall is the leading cause of injury related mortality and morbidity among elderly of Bangladesh. A significant amount of deaths also occurred due to RTI and suicide. Further in-depth research and interventions are needed to minimize the preventable burden of fatality and morbidity among this vulnerable-group. Key messages Elderly, Injury, Epidemiology, Bangladesh [ABSTRACT FROM AUTHOR]

Background The positive cross-sectional association between health and SES often strengthens at younger ages before peaking at middle ages and then weakening at older ages. Selective mortality is a possible reason for the weakening relationship at older ages but current evidence for this is limited. Methods This paper uncovers the changing nature of the inter-dependence between SES and health over the lifecycle by further developing and applying longitudinal inequality decomposition techniques which account for mortality. We examine changes in SES-related health inequality for rolling age cohorts by gender for Australia (using the Household, Income and Labour Dynamics in Australia (HILDA) survey) and the United Kingdom (using the Understanding Society survey). Results We find for young men in both countries that the simultaneous co-movement in both health and income plays the major role in increasing health inequalities. At middle ages the poor start to lose health more quickly than the rich but at older ages selective mortality plays the major role with the poor more likely to die than the rich which also has an indirect effect of making morbidity losses seem less concentrated among the poor. Conclusions Selective mortality plays a major role in weakening the relationship between SES and health at older ages. Past studies have missed identifying the full effect of selective mortality. Key messages SES-related health inequalities accumulate throughout the lifecycle, even in older ages. [ABSTRACT FROM AUTHOR]

Background Improvement in people's knowledge of HIV/AIDS and reduction in stigma and discrimination have been key factors in the fight against HIV/AIDS. The objective of the present paper is to examine the mediation effect of comprehensive knowledge on people's attitudes towards HIV positives in India. Methods Data for the study have been drawn from nationally representative India's National Family Health Survey, 2015-16. Statistical methods of data analysis included odds ratio and logistic regression models. Results Two in every five men and of women with comprehensive knowledge held a positive attitude towards PLHIVs. The people in rural areas and females against their counterparts are less likely (OR: 0.85, p < 0.05) to have positive attitude. The mediation by comprehensive knowledge, contributed to 46% of direct effect from gender perspective on comprehensive positive attitude. Among males, aged group 25-44 and among females age <25 had most positive attitude towards people living with HIV (PLHIV) as compared to their counterparts. Conclusions There existed a difference between males and females in attaining comprehensive knowledge and having comprehensive positive attitude. The mediation by comprehensive knowledge contributed, though partially, direct effects from gender perspective on comprehensive positive attitude. The findings are programmatically relevant to device content analysis of the material in the information, education and communication strategies. Key messages The mediation effect of comprehensive knowledge on people's attitudes towards HIV positives has been substantial in India. [ABSTRACT FROM AUTHOR]

Background Nigeria, in 2019, implemented an Integrated Measles and Meningitis A vaccination campaign (IVC) majorly in the northern part of the country including the Federal Capital Territory (FCT), Abuja. The campaign was necessary to interrupt the transmission of these diseases in unimmunized under-fives. FCT aligned with this novel approach to reach its target population of 692697 children aged 9-59 months and 651951 children aged 1-5 years with the Measles and Men A vaccines respectively. Given the integration, FCT was going to generate twice the volume of wastes compared to previous stand-alone campaigns. This paper aims to document the best practices FCT used to incinerate the large volume of sharp wastes generated during the IVC as well as outstanding wastes generated during routine immunization (RI) services. Methods A waste management working group was inaugurated at the state level to coordinate all waste management activities in FCT. They developed a waste management plan outlining the expected amount of wastes to be generated, cost implications, mapping of incineration sites and modalities of evacuation and incineration. Result A total of 20000 safety boxes generated from the integrated Measles and Men A vaccination campaign (17645) and RI (2355) were incinerated. Conclusion FCT has incinerated all sharps generated during the 2019 integrated Measles and Men A vaccination campaign as well as outstanding routine immunization wastes. Key message Coordinated waste management activities are required to continuously manage RI wastes. [ABSTRACT FROM AUTHOR]

Background The Physical Activity Cohort Repository (PACe) project was initiated by the Epidemiology Council of the International Society of Physical Activity (ISPAH). The project will create a central database of cohort studies with at least 1,000 participants that have collected data on physical activity (self-reported and/or accelerometer measures) at three or more timepoints, to facilitate physical activity epidemiology research worldwide. Methods A scoping review commenced in January 2020 to identify relevant cohort studies in PubMed and Web of Science. All titles and abstracts identified in the search were screened, and data are to be extracted by two authors. Data to be extracted include: cohort size; location; years of baseline recruitment; broad demographics of the cohort; how and when physical activity was measured; clinical measures; biological samples; cohort profile publication; and information on how to request access to the data (if applicable). Results Our scoping search identified 8,992 articles. From the 851 papers from studies meeting the eligibility criteria, we have identified 409 unique cohort studies. We anticipate having the PACe online database completed and accessible by the end of 2021. Conclusions The PACe will help to maximize the investments made into cohort studies, by encouraging researchers to return to mature cohorts and apply contemporary methods to physical activity-related research questions. The PACe will also facilitate international collaborations and help build capacity in low- and middle-income countries. Key messages The PACe will be a searchable catalogue of cohort studies that have included self-reported and/or accelerometer measures of physical activity. [ABSTRACT FROM AUTHOR]

Focus of presentation Missing data are ubiquitous in medical research. Although there is increasing guidance on how to handle missing data, practice is changing slowly and misapprehensions abound, particularly in observational research. We present a practical framework for handling and reporting the analysis of incomplete data in observational studies, which we illustrate using a case study from the Avon Longitudinal Study of Parents and Children. Findings The framework consists of three steps: 1) Develop an analysis plan specifying the analysis model and how missing data are going to be addressed. Important considerations are whether a complete records analysis is likely to be valid, whether multiple imputation or an alternative approach is likely to offer benefits, and whether a sensitivity analysis regarding the missingness mechanism is required. 2) Explore the data, checking the methods outlined in the analysis plan are appropriate, and conduct the pre-planned analysis. 3) Report the results, including a description of the missing data, details on how missing data were addressed, and the results from all analyses, interpreted in light of the missing data and clinical relevance. Conclusions/Implications This framework encourages researchers to think carefully about their missing data and be transparent about the potential effect on the study results. This will increase confidence in the reliability and reproducibility of results from published papers. Key messages Researchers need to develop a plan for missing data prior to conducting their analysis, and be transparent about how they handled the missing data and its potential effect when reporting their results. [ABSTRACT FROM AUTHOR]

Focus of Presentation Rheumatic fever (RF) and rheumatic heart disease (RHD) are endemic among Indigenous Australians. End RHD in Australia: Study of Epidemiology (ERASE) aimed to characterize contemporary RF/RHD epidemiology. Using multi-jurisdictional linked data from several administrative sources, we undertook sub-studies covering diverse epidemiological questions, requiring substantial methods development. Mixed methods further identified barriers/facilitators to inform system redesign. Our multi-disciplinary collaboration supported diverse initiatives to contribute to policy at government, service and community/stakeholder levels. We show how findings from ERASE were applied/translated to address the impact of RF/RHD in Australia. Findings Academic: >15 papers and commentaries/editorials provided the backbone to translational outputs and methods sharing. PhD students have ongoing projects using ERASE datasets. Advocacy: ERASE epidemiological and economic information supported the Endgame Strategy (roadmap for eliminating RHD in Australia by 2031) presented to government. Health professionals: ERASE data contributed to Australian RF/RHD guidelines. Slides of results/interpretation are publically-available on the RHDAustralia website. Student lectures integrate biomedical and culturally-informed perspectives. Indigenous stakeholder engagement: involves (1)presentations to peak Indigenous-controlled organisations (2)co-designed resources (booklets/slides) for capacity-building of RHDAustralia's national Champions4Change network (3)research workshops to promote two-way learning and health literacy/numeracy. Challenges remain regarding strengths-based approaches when reporting high disparities. Conclusions/Implications Strong translational commitment and national multi-disciplinary networks of Indigenous and non-Indigenous collaborators ensured ERASE generated multiple outputs that continue to inform training, practice, policy and community health literacy. Key messages Build translation and broad collaboration into study from the start. [ABSTRACT FROM AUTHOR]

Background Hepatitis B virus (HBV) infections are a major global health burden. Although a vaccine exists for HBV, some developing nations still struggle to reduce prevalence. While the countries within the Indian Subcontinent have vaccination programs, HBV prevalence varies. It is important to identify population groups at risk. Methods Several databases were systematically searched for papers appropriate studies. Studies were included if the sample population lived within one of the selected countries and had no other existing health issues. The studies were assessed for risk of bias, and pooled estimates were created. Subgroups which were assessed included countries, sex, rural and urban populations. Results The pooled prevalence of all studies (N = 56) was 4%. For the countries with multiple included studies, the prevalence for India, Pakistan, Bangladesh and Nepal were calculated as 3%, 6%, 5% and 1% respectively. Most of the studies were assessed as being of reasonable quality. There was a chance that publication bias was present. Conclusions While some of the populations had the expected prevalence for the country in which the study was set, other subpopulations had higher prevalence. The highest HBV prevalence was found in displaced persons and those engaging in risk taking behaviours, however some high prevalence groups did not have a clear reason. Key messages Although HBV prevalence in general is lower at present than in the past, there are still at-risk groups with higher prevalence than the general population. [ABSTRACT FROM AUTHOR]

Background Australia has eight state-based ambulance services and New Zealand (NZ) has two. Significant variation between their datasets compromises cross-border research opportunities and translation of research to improve patient care. Ambulance data harmonisation has occurred in the United States and United Kingdom however, to-date no data harmonisation has occurred in Australia. This study aims to compare ambulance service variables in Australia and NZ to identify opportunities and barriers for harmonisation. Method Available 2019 variables were mapped to each other and several international standardized terminology systems to identify variations and similarities in variable names and definitions, and harmonisation opportunities. Results Four Australian ambulance services used one electronic patient care record (ePCR) system, three used other ePCR systems, one used paper-based records and both NZ services used one ePCR system. Only the NZ services had mapped their variables to two international standardised terminology systems. Barriers to harmonisation included the variables collected, the variable definitions and the variable naming convention. The core variables available for mapping varied and numbered from 27-69. Differences included similar variable names having different definitions, variables that should have different definitions having the same, and naming convention for similar/same variables differing between services. Conclusions Ambulance service data harmonisation in Australia and NZ is possible and presents significant opportunities for improvement in patient outcomes and performance audit. It would facilitate quality, large-scale, high-impact collaborative national and international research. Key Message There is an opportunity for Australian and NZ ambulance services to harmonise their data to conduct large scale international research. [ABSTRACT FROM AUTHOR]